视觉回环检测的多约束深度距离学习方法

In visual loop closure detection under strong scene changes, the feature descriptors extracted by the existing deep learning methods cannot be distinguished well. Aiming at this problem, the multi-constraint distance relationship is analyzed, and a multi-constraint deep distance learning method for visual loop closure detection is proposed. Firstly, the original images are mapped to feature descriptors by any convolutional neural network in the low-dimensional feature space. Then, a multi-constraint loss function is proposed to constrain the distance relationships among feature descriptors, and a multi-constraint training sample set is automatically constructed online to extract more discriminative low-dimensional feature descriptors. Experiments on New College and TUM datasets show that the proposed method improves the performance of loop closure detection under strong scene changes.